Detailed Survival analyis of the Survival lung data.

Libraries

library(survival)
library(FRESA.CAD)
## Loading required package: Rcpp
## Loading required package: stringr
## Loading required package: miscTools
## Loading required package: Hmisc
## 
## Attaching package: 'Hmisc'
## The following objects are masked from 'package:base':
## 
##     format.pval, units
## Loading required package: pROC
## Type 'citation("pROC")' for a citation.
## 
## Attaching package: 'pROC'
## The following objects are masked from 'package:stats':
## 
##     cov, smooth, var
op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('keep.trailing.zeros',TRUE)

Libraries

data(lung)
## Warning in data(lung): data set 'lung' not found
lung$inst <- NULL
lung$status <- lung$status - 1
lung <- lung[complete.cases(lung),]

pander::pander(table(lung$status))
0 1
47 121
pander::pander(summary(lung$time))
Min. 1st Qu. Median Mean 3rd Qu. Max.
5 175 268 310 416 1022

Exploring Raw Features with RRPlot

convar <- colnames(lung)[lapply(apply(lung,2,unique),length) > 10]
convar <- convar[convar != "time"]
topvar <- univariate_BinEnsemble(lung[,c("status",convar)],"status")
pander::pander(topvar)
age wt.loss
0.106 0.106
topv <- min(5,length(topvar))
topFive <- names(topvar)[1:topv]
RRanalysis <- list();
idx <- 1
for (topf in topFive)
{
  RRanalysis[[idx]] <- RRPlot(cbind(lung$status,lung[,topf]),
                              atRate=c(0.90),
                  timetoEvent=lung$time,
                  title=topf,
#                  plotRR=FALSE
                  )
  idx <- idx + 1
}

names(RRanalysis) <- topFive

Reporting the Metrics

ROCAUC <- NULL
CstatCI <- NULL
LogRangp <- NULL
Sensitivity <- NULL
Specificity <- NULL

for (topf in topFive)
{
  CstatCI <- rbind(CstatCI,RRanalysis[[topf]]$c.index$cstatCI)
  LogRangp <- rbind(LogRangp,RRanalysis[[topf]]$surdif$pvalue)
  Sensitivity <- rbind(Sensitivity,RRanalysis[[topf]]$ROCAnalysis$sensitivity)
  Specificity <- rbind(Specificity,RRanalysis[[topf]]$ROCAnalysis$specificity)
  ROCAUC <- rbind(ROCAUC,RRanalysis[[topf]]$ROCAnalysis$aucs)
}
rownames(CstatCI) <- topFive
rownames(LogRangp) <- topFive
rownames(Sensitivity) <- topFive
rownames(Specificity) <- topFive
rownames(ROCAUC) <- topFive

pander::pander(ROCAUC)
  est lower upper
age 0.590 0.493 0.688
wt.loss 0.553 0.455 0.651
pander::pander(CstatCI)
  mean.C Index median lower upper
age 0.558 0.559 0.505 0.612
wt.loss 0.511 0.511 0.453 0.572
pander::pander(LogRangp)
age 0.919
wt.loss 0.358
pander::pander(Sensitivity)
  est lower upper
age 0.1157 0.0647 0.187
wt.loss 0.0496 0.0184 0.105
pander::pander(Specificity)
  est lower upper
age 0.872 0.743 0.952
wt.loss 0.894 0.769 0.965
meanMatrix <- cbind(ROCAUC[,1],CstatCI[,1],Sensitivity[,1],Specificity[,1])
colnames(meanMatrix) <- c("ROCAUC","C-Stat","Sen","Spe")
pander::pander(meanMatrix)
  ROCAUC C-Stat Sen Spe
age 0.590 0.558 0.1157 0.872
wt.loss 0.553 0.511 0.0496 0.894

Modeling

ml <- BSWiMS.model(Surv(time,status)~1,data=lung,NumberofRepeats = 10)

[+++++++++++++++++++++++++++++]..

sm <- summary(ml)
pander::pander(sm$coefficients)
Table continues below
  Estimate lower HR upper u.Accuracy r.Accuracy
ph.ecog 4.32e-01 1.194 1.541 1.988 0.679 0.649
sex -4.59e-01 0.456 0.632 0.876 0.649 0.679
pat.karno -1.77e-03 0.997 0.998 1.000 0.506 0.720
ph.karno -4.64e-07 1.000 1.000 1.000 0.577 0.720
age 4.57e-08 1.000 1.000 1.000 0.565 0.720
Table continues below
  full.Accuracy u.AUC r.AUC full.AUC IDI NRI
ph.ecog 0.601 0.601 0.620 0.600 0.0449 0.405
sex 0.601 0.620 0.601 0.600 0.0285 0.478
pat.karno 0.506 0.585 0.500 0.585 0.0292 0.342
ph.karno 0.577 0.570 0.500 0.570 0.0143 0.280
age 0.565 0.549 0.500 0.549 0.0162 0.195
  z.IDI z.NRI Delta.AUC Frequency
ph.ecog 3.33 2.48 -0.02005 1.0
sex 2.76 2.85 -0.00167 1.0
pat.karno 2.44 2.24 0.08546 1.0
ph.karno 2.22 1.64 0.06998 0.8
age 1.97 1.14 0.04871 0.1

Cox Model Performance

Here we evaluate the model using the RRPlot() function.

The evaluation of the raw Cox model with RRPlot()

Here we will use the predicted event probability assuming a baseline hazard for events withing 5 years

timeinterval <- 2*mean(subset(lung,status==1)$time)

h0 <- sum(lung$status & lung$time <= timeinterval)
h0 <- h0/sum((lung$time > timeinterval) | (lung$status==1))
pander::pander(t(c(h0=h0,timeinterval=timeinterval)),caption="Initial Parameters")
Initial Parameters
h0 timeinterval
0.85 578
index <- predict(ml,lung)

rdata <- cbind(lung$status,ppoisGzero(index,h0))

rrAnalysisTrain <- RRPlot(rdata,atRate=c(0.90),
                     timetoEvent=lung$time,
                     title="Raw Train: Lung Cancer",
                     ysurvlim=c(0.00,1.0),
                     riskTimeInterval=timeinterval)

Expected time to event

toinclude <- rdata[,1]==1 
obstiemToEvent <- lung$time
tmin<-min(obstiemToEvent)
sum(toinclude)

[1] 121

timetoEvent <- meanTimeToEvent(rdata[,2],timeinterval)
tmax<-max(c(obstiemToEvent,timetoEvent))
lmfit <- lm(obstiemToEvent[toinclude]~0+timetoEvent[toinclude])
sm <- summary(lmfit)
pander::pander(sm)
  Estimate Std. Error t value Pr(>|t|)
timetoEvent[toinclude] 0.808 0.0509 15.9 2.92e-31
Fitting linear model: obstiemToEvent[toinclude] ~ 0 + timetoEvent[toinclude]
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
121 200 0.677 0.675
plot(timetoEvent,obstiemToEvent,
     col=1+rdata[,1],
     xlab="Expected time",
     ylab="Observed time",
     main="Expected vs. Observed",
     xlim=c(tmin,tmax),
     ylim=c(tmin,tmax),
     log="xy")
lines(x=c(tmin,tmax),y=lmfit$coefficients*c(tmin,tmax),lty=1,col="blue")
txt <- bquote(paste(R^2 == .(round(sm$r.squared,3))))
text(tmin+0.005*(tmax-tmin),tmax,txt,cex=0.7)
text(tmin+0.015*(tmax-tmin),tmax,sprintf("Slope=%4.3f",sm$coefficients[1]),cex=0.7)
legend("bottomright",legend=c("No Event","Event","Linear fit"),
             pch=c(1,1,-1),
             col=c(1,2,"blue"),
             lty=c(-1,-1,1)
             )

MADerror2 <- mean(abs(timetoEvent[toinclude]-obstiemToEvent[toinclude]))
pander::pander(MADerror2)

163

Uncalibrated Performance Report

pander::pander(t(rrAnalysisTrain$keyPoints),caption="Threshold values")
Threshold values
  @:0.9 @MAX_BACC @MAX_RR @SPE100 p(0.5)
Thr 0.649 0.478 3.39e-01 3.39e-01 0.493
RR 1.214 1.742 6.85e+01 6.85e+01 1.270
RR_LCI 1.014 1.260 1.44e-01 1.44e-01 1.037
RR_UCI 1.454 2.408 3.26e+04 3.26e+04 1.555
SEN 0.314 0.826 1.00e+00 1.00e+00 0.612
SPE 0.830 0.511 1.91e-01 1.91e-01 0.596
BACC 0.572 0.669 5.96e-01 5.96e-01 0.604
NetBenefit 0.138 0.470 6.04e-01 6.04e-01 0.331
pander::pander(t(rrAnalysisTrain$OERatio$estimate),caption="O/E Ratio")
O/E Ratio
O/E Low Upper p.value
1.57 1.3 1.87 3.7e-06
pander::pander(t(rrAnalysisTrain$OE95ci),caption="O/E Mean")
O/E Mean
mean 50% 2.5% 97.5%
1.23 1.23 1.19 1.27
pander::pander(t(rrAnalysisTrain$OAcum95ci),caption="O/Acum Mean")
O/Acum Mean
mean 50% 2.5% 97.5%
1.27 1.27 1.26 1.29
pander::pander(rrAnalysisTrain$c.index$cstatCI,caption="C. Index")
mean.C Index median lower upper
0.651 0.651 0.589 0.711
pander::pander(t(rrAnalysisTrain$ROCAnalysis$aucs),caption="ROC AUC")
ROC AUC
est lower upper
0.691 0.598 0.784
pander::pander((rrAnalysisTrain$ROCAnalysis$sensitivity),caption="Sensitivity")
Sensitivity
est lower upper
0.314 0.233 0.405
pander::pander((rrAnalysisTrain$ROCAnalysis$specificity),caption="Specificity")
Specificity
est lower upper
0.83 0.692 0.924
pander::pander(t(rrAnalysisTrain$thr_atP),caption="Probability Thresholds")
Probability Thresholds
90%
0.648
pander::pander(rrAnalysisTrain$surdif,caption="Logrank test")
Logrank test Chisq = 7.945448 on 1 degrees of freedom, p = 0.004821
  N Observed Expected (O-E)^2/E (O-E)^2/V
class=0 122 83 95.6 1.65 7.95
class=1 46 38 25.4 6.22 7.95

Cox Calibration

op <- par(no.readonly = TRUE)


calprob <- CoxRiskCalibration(ml,lung,"status","time")

pander::pander(c(h0=calprob$h0,
                 Gain=calprob$hazardGain,
                 DeltaTime=calprob$timeInterval),
               caption="Cox Calibration Parameters")
h0 Gain DeltaTime
1.55 1.83 863

The RRplot() of the calibrated model

h0 <- calprob$h0
timeinterval <- calprob$timeInterval;

rdata <- cbind(lung$status,calprob$prob)


rrAnalysisTrain <- RRPlot(rdata,atRate=c(0.90),
                     timetoEvent=lung$time,
                     title="Train Cal: Lung",
                     ysurvlim=c(0.00,1.0),
                     riskTimeInterval=timeinterval)

Expected time to event

timetoEvent <- meanTimeToEvent(rdata[,2],timeinterval)
tmax<-max(c(obstiemToEvent,timetoEvent))
lmfit <- lm(obstiemToEvent[toinclude]~0+timetoEvent[toinclude])
sm <- summary(lmfit)
pander::pander(sm)
  Estimate Std. Error t value Pr(>|t|)
timetoEvent[toinclude] 0.99 0.0623 15.9 2.92e-31
Fitting linear model: obstiemToEvent[toinclude] ~ 0 + timetoEvent[toinclude]
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
121 200 0.677 0.675
plot(timetoEvent,obstiemToEvent,
     col=1+rdata[,1],
     xlab="Expected time",
     ylab="Observed time",
     main="Expected vs. Observed",
     xlim=c(tmin,tmax),
     ylim=c(tmin,tmax),
     log="xy")
lines(x=c(tmin,tmax),y=lmfit$coefficients*c(tmin,tmax),lty=1,col="blue")
txt <- bquote(paste(R^2 == .(round(sm$r.squared,3))))
text(tmin+0.005*(tmax-tmin),tmax,txt,cex=0.7)
text(tmin+0.015*(tmax-tmin),tmax,sprintf("Slope=%4.3f",sm$coefficients[1]),cex=0.7)
legend("bottomright",legend=c("No Event","Event","Linear fit"),
             pch=c(1,1,-1),
             col=c(1,2,"blue"),
             lty=c(-1,-1,1)
             )

MADerror2 <-c(MADerror2,mean(abs(timetoEvent-obstiemToEvent)))
pander::pander(MADerror2)

163 and 165

Calibrated Train Performance

pander::pander(t(rrAnalysisTrain$keyPoints),caption="Threshold values")
Threshold values
  @:0.9 @MAX_BACC @MAX_RR @SPE100 p(0.5)
Thr 0.8523 0.695 5.31e-01 5.31e-01 5.31e-01
RR 1.2142 1.742 6.85e+01 6.85e+01 6.85e+01
RR_LCI 1.0143 1.260 1.44e-01 1.44e-01 1.44e-01
RR_UCI 1.4536 2.408 3.26e+04 3.26e+04 3.26e+04
SEN 0.3140 0.826 1.00e+00 1.00e+00 1.00e+00
SPE 0.8298 0.511 1.91e-01 1.91e-01 1.91e-01
BACC 0.5719 0.669 5.96e-01 5.96e-01 5.96e-01
NetBenefit -0.0484 0.283 4.64e-01 4.64e-01 4.64e-01
pander::pander(t(rrAnalysisTrain$OERatio$estimate),caption="O/E Ratio")
O/E Ratio
O/E Low Upper p.value
1.28 1.06 1.53 0.00868
pander::pander(t(rrAnalysisTrain$OE95ci),caption="O/E Mean")
O/E Mean
mean 50% 2.5% 97.5%
1.01 1 0.971 1.04
pander::pander(t(rrAnalysisTrain$OAcum95ci),caption="O/Acum Mean")
O/Acum Mean
mean 50% 2.5% 97.5%
1.02 1.02 1.01 1.02
pander::pander(rrAnalysisTrain$c.index$cstatCI,caption="C. Index")
mean.C Index median lower upper
0.651 0.652 0.596 0.71
pander::pander(t(rrAnalysisTrain$ROCAnalysis$aucs),caption="ROC AUC")
ROC AUC
est lower upper
0.691 0.598 0.784
pander::pander((rrAnalysisTrain$ROCAnalysis$sensitivity),caption="Sensitivity")
Sensitivity
est lower upper
0.314 0.233 0.405
pander::pander((rrAnalysisTrain$ROCAnalysis$specificity),caption="Specificity")
Specificity
est lower upper
0.83 0.692 0.924
pander::pander(t(rrAnalysisTrain$thr_atP),caption="Probability Thresholds")
Probability Thresholds
90%
0.851
pander::pander(rrAnalysisTrain$surdif,caption="Logrank test")
Logrank test Chisq = 7.945448 on 1 degrees of freedom, p = 0.004821
  N Observed Expected (O-E)^2/E (O-E)^2/V
class=0 122 83 95.6 1.65 7.95
class=1 46 38 25.4 6.22 7.95

Cross-Validation

rcv <- randomCV(theData=lung,
                theOutcome = Surv(time,status)~1,
                fittingFunction=BSWiMS.model, 
                trainFraction = 0.95,
                repetitions=200,
                classSamplingType = "Pro"
         )

.[+++].[++].[+++].[++].[+++].[+++].[++].[+++].[+++].[+]10 Tested: 77 Avg. Selected: 3.5 Min Tests: 1 Max Tests: 3 Mean Tests: 1.298701 . MAD: 0.4783289

.[++].[+++].[++].[+++].[+++].[+++].[+++].[++++].[++].[+++]20 Tested: 114 Avg. Selected: 3.65 Min Tests: 1 Max Tests: 6 Mean Tests: 1.754386 . MAD: 0.4773353

.[+-].[++++].[++].[+++].[++].[++].[++].[++].[+++].[++++]30 Tested: 138 Avg. Selected: 3.6 Min Tests: 1 Max Tests: 7 Mean Tests: 2.173913 . MAD: 0.4785659

.[+++].[+++].[++-].[+++].[++].[++].[+++].[++].[+++].[++]40 Tested: 150 Avg. Selected: 3.575 Min Tests: 1 Max Tests: 8 Mean Tests: 2.666667 . MAD: 0.4800767

.[+++].[++].[+++-].[+++].[++++].[+++].[+++].[++++].[+++].[+++]50 Tested: 157 Avg. Selected: 3.66 Min Tests: 1 Max Tests: 9 Mean Tests: 3.184713 . MAD: 0.4793113

.[+-].[+++].[+++].[++++].[+++].[+++].[++++].[+++].[+++].[+++]60 Tested: 158 Avg. Selected: 3.716667 Min Tests: 1 Max Tests: 10 Mean Tests: 3.797468 . MAD: 0.4788951

.[++].[+++].[+++].[++++].[++].[++].[+++].[+++].[+++].[++]70 Tested: 160 Avg. Selected: 3.714286 Min Tests: 1 Max Tests: 13 Mean Tests: 4.375 . MAD: 0.4792667

.[+++].[+++].[++-].[++].[+++].[+++].[+++].[+++].[+].[++]80 Tested: 166 Avg. Selected: 3.6875 Min Tests: 1 Max Tests: 13 Mean Tests: 4.819277 . MAD: 0.475646

.[+++].[++].[++].[+].[+++].[+++].[+++].[+++].[+++].[+++]90 Tested: 168 Avg. Selected: 3.677778 Min Tests: 1 Max Tests: 14 Mean Tests: 5.357143 . MAD: 0.475111

.[+++].[++].[+++].[++++].[+++].[++++].[+++].[+++].[+++].[++++]100 Tested: 168 Avg. Selected: 3.71 Min Tests: 1 Max Tests: 16 Mean Tests: 5.952381 . MAD: 0.4752537

.[++++].[++++].[+++].[+++].[+++].[+].[++].[++].[+].[+++]110 Tested: 168 Avg. Selected: 3.7 Min Tests: 1 Max Tests: 16 Mean Tests: 6.547619 . MAD: 0.4750302

.[+++].[+++].[+++].[+++].[++].[+++].[+].[+++].[++].[++]120 Tested: 168 Avg. Selected: 3.683333 Min Tests: 1 Max Tests: 16 Mean Tests: 7.142857 . MAD: 0.4748065

.[+++].[++].[+++].[+++].[+++].[+++].[+++].[+++].[++].[++]130 Tested: 168 Avg. Selected: 3.684615 Min Tests: 2 Max Tests: 17 Mean Tests: 7.738095 . MAD: 0.4749498

.[+++].[+++].[+++].[+].[++].[+++].[+++].[+++].[+++].[+++]140 Tested: 168 Avg. Selected: 3.685714 Min Tests: 2 Max Tests: 17 Mean Tests: 8.333333 . MAD: 0.4746038

.[+++].[+++].[+++].[+++].[+-].[+++].[+++].[++].[+++].[+++]150 Tested: 168 Avg. Selected: 3.686667 Min Tests: 2 Max Tests: 20 Mean Tests: 8.928571 . MAD: 0.4746182

.[++].[+++].[++].[+].[+++].[++].[++].[++].[+].[+++]160 Tested: 168 Avg. Selected: 3.65 Min Tests: 2 Max Tests: 22 Mean Tests: 9.52381 . MAD: 0.4744816

.[++].[+++].[+++].[+++].[+++].[+++].[+++].[+++].[+++].[++]170 Tested: 168 Avg. Selected: 3.658824 Min Tests: 3 Max Tests: 23 Mean Tests: 10.11905 . MAD: 0.4743399

.[+++].[++].[+++].[+++].[+].[++].[++].[+++].[+++].[++]180 Tested: 168 Avg. Selected: 3.644444 Min Tests: 3 Max Tests: 23 Mean Tests: 10.71429 . MAD: 0.4743689

.[++].[+++].[++].[++].[++].[+++].[+++].[+++].[+++].[+++]190 Tested: 168 Avg. Selected: 3.642105 Min Tests: 3 Max Tests: 23 Mean Tests: 11.30952 . MAD: 0.4744639

.[+++].[+++].[+++].[+++].[++].[++].[+++].[+++].[++].[+++]200 Tested: 168 Avg. Selected: 3.645 Min Tests: 3 Max Tests: 26 Mean Tests: 11.90476 . MAD: 0.4742153

stp <- rcv$survTestPredictions
stp <- stp[!is.na(stp[,4]),]

bbx <- boxplot(unlist(stp[,1])~rownames(stp),plot=FALSE)
times <- bbx$stats[3,]
status <- boxplot(unlist(stp[,2])~rownames(stp),plot=FALSE)$stats[3,]
prob <- ppoisGzero(boxplot(unlist(stp[,4])~rownames(stp),plot=FALSE)$stats[3,],h0)

rdatacv <- cbind(status,prob)
rownames(rdatacv) <- bbx$names
names(times) <- bbx$names

rrAnalysisTest <- RRPlot(rdatacv,atRate=c(0.90),
                     timetoEvent=times,
                     title="Test: Lung Cancer",
                     ysurvlim=c(0.00,1.0),
                     riskTimeInterval=timeinterval)

Cross-Validation Test Performance

pander::pander(t(rrAnalysisTest$keyPoints),caption="Threshold values")
Threshold values
  @:0.9 @MAX_BACC @MAX_RR @SPE100 p(0.5)
Thr 0.8629 0.680 0.587 0.505 0.505
RR 1.1859 2.738 3.510 1.000 1.000
RR_LCI 0.9720 1.385 1.473 0.000 0.000
RR_UCI 1.4469 5.413 8.365 0.000 0.000
SEN 0.1983 0.950 0.967 1.000 1.000
SPE 0.8936 0.319 0.298 0.000 0.000
BACC 0.5460 0.635 0.632 0.500 0.500
NetBenefit -0.0443 0.279 0.418 0.435 0.435
pander::pander(t(rrAnalysisTest$OERatio$estimate),caption="O/E Ratio")
O/E Ratio
O/E Low Upper p.value
1.25 1.04 1.5 0.0167
pander::pander(t(rrAnalysisTest$OE95ci),caption="O/E Mean")
O/E Mean
mean 50% 2.5% 97.5%
0.991 0.991 0.959 1.02
pander::pander(t(rrAnalysisTest$OAcum95ci),caption="O/Acum Mean")
O/Acum Mean
mean 50% 2.5% 97.5%
0.98 0.98 0.972 0.989
pander::pander(rrAnalysisTest$c.index$cstatCI,caption="C. Index")
mean.C Index median lower upper
0.594 0.594 0.53 0.653
pander::pander(t(rrAnalysisTest$ROCAnalysis$aucs),caption="ROC AUC")
ROC AUC
est lower upper
0.611 0.512 0.711
pander::pander((rrAnalysisTest$ROCAnalysis$sensitivity),caption="Sensitivity")
Sensitivity
est lower upper
0.198 0.131 0.281
pander::pander((rrAnalysisTest$ROCAnalysis$specificity),caption="Specificity")
Specificity
est lower upper
0.894 0.769 0.965
pander::pander(t(rrAnalysisTest$thr_atP),caption="Probability Thresholds")
Probability Thresholds
90%
0.863
pander::pander(rrAnalysisTest$surdif,caption="Logrank test")
Logrank test Chisq = 2.422523 on 1 degrees of freedom, p = 0.119602
  N Observed Expected (O-E)^2/E (O-E)^2/V
class=0 139 97 103 0.355 2.42
class=1 29 24 18 2.035 2.42

Calibrating the test results

rdatacv <- cbind(status,prob,times)
calprob <- CalibrationProbPoissonRisk(rdatacv)

pander::pander(c(h0=calprob$h0,
                 Gain=calprob$hazardGain,
                 DeltaTime=calprob$timeInterval),
               caption="Cox Calibration Parameters")
h0 Gain DeltaTime
0.85 1 874
timeinterval <- calprob$timeInterval;

rdata <- cbind(status,calprob$prob)


rrAnalysisTest <- RRPlot(rdata,atRate=c(0.90),
                     timetoEvent=times,
                     title="Calibrated Test: Lung",
                     ysurvlim=c(0.00,1.0),
                     riskTimeInterval=timeinterval)

Calibrated Test Performance

pander::pander(t(rrAnalysisTest$keyPoints),caption="Threshold values")
Threshold values
  @:0.9 @MAX_BACC @MAX_RR @SPE100 p(0.5)
Thr 0.8629 0.680 0.587 0.505 0.505
RR 1.1859 2.738 3.510 1.000 1.000
RR_LCI 0.9720 1.385 1.473 0.000 0.000
RR_UCI 1.4469 5.413 8.365 0.000 0.000
SEN 0.1983 0.950 0.967 1.000 1.000
SPE 0.8936 0.319 0.298 0.000 0.000
BACC 0.5460 0.635 0.632 0.500 0.500
NetBenefit -0.0443 0.279 0.418 0.435 0.435
pander::pander(t(rrAnalysisTest$OERatio$estimate),caption="O/E Ratio")
O/E Ratio
O/E Low Upper p.value
1.27 1.05 1.52 0.0104
pander::pander(t(rrAnalysisTest$OE95ci),caption="O/E Mean")
O/E Mean
mean 50% 2.5% 97.5%
1 1 0.971 1.04
pander::pander(t(rrAnalysisTest$OAcum95ci),caption="O/Acum Mean")
O/Acum Mean
mean 50% 2.5% 97.5%
0.981 0.981 0.973 0.99
pander::pander(rrAnalysisTest$c.index$cstatCI,caption="C. Index")
mean.C Index median lower upper
0.594 0.593 0.53 0.659
pander::pander(t(rrAnalysisTest$ROCAnalysis$aucs),caption="ROC AUC")
ROC AUC
est lower upper
0.611 0.512 0.711
pander::pander((rrAnalysisTest$ROCAnalysis$sensitivity),caption="Sensitivity")
Sensitivity
est lower upper
0.198 0.131 0.281
pander::pander((rrAnalysisTest$ROCAnalysis$specificity),caption="Specificity")
Specificity
est lower upper
0.894 0.769 0.965
pander::pander(t(rrAnalysisTest$thr_atP),caption="Probability Thresholds")
Probability Thresholds
90%
0.863
pander::pander(rrAnalysisTest$surdif,caption="Logrank test")
Logrank test Chisq = 2.422523 on 1 degrees of freedom, p = 0.119602
  N Observed Expected (O-E)^2/E (O-E)^2/V
class=0 139 97 103 0.355 2.42
class=1 29 24 18 2.035 2.42